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Figure 1.
Creation of Surge Capacity by Reverse Triage
Creation of Surge Capacity by Reverse Triage

Histogram depicts capacity within 24 hours in the pediatric hospital units. Bars for the psychiatry unit and total capacity are patterned to reflect the proportionate contribution of the psychiatry unit to capacity potential after reverse triage. ICU indicates intensive care unit.

Figure 2.
Effect of Pediatric Surge Strategies Across 96 Hours for All Services
Effect of Pediatric Surge Strategies Across 96 Hours for All Services

Unstaffed licensed beds are shown ghosted to underscore the need for incremental staff or alteration in the standard of care. Line reflects the effect of routine admissions from the emergency department on surge creation (ie, net surge capacity). T0 indicates the day of the disaster; T1, the first 24 hours; T2, within 48 hours; T3, within 72 hours; and T4, within 96 hours.

Table 1.  
Sample Demographics
Sample Demographics
Table 2.  
Frequency of PCIs
Frequency of PCIs
Table 3.  
Logistic Regression ORs for Early Discharge
Logistic Regression ORs for Early Discharge
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Kelen  GD, McCarthy  ML, Kraus  CK,  et al.  Creation of surge capacity by early discharge of hospitalized patients at low risk for untoward events.  Disaster Med Public Health Prep. 2009;3(2)(suppl):S10-S16.PubMedGoogle ScholarCrossref
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Kelen  GD, Sauer  L, Clattenburg  E, Lewis-Newby  M, Fackler  J.  Pediatric disposition classification (reverse triage) system to create surge capacity.  Disaster Med Public Health Prep. 2015;9(3):283-290.PubMedGoogle ScholarCrossref
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Boutis  K, Stephens  D, Lam  K, Ungar  WJ, Schuh  S.  The impact of SARS on a tertiary care pediatric emergency department.  CMAJ. 2004;171(11):1353-1358.PubMedGoogle ScholarCrossref
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Quinn  B, Baker  R, Pratt  J.  Hurricane Andrew and a pediatric emergency department.  Ann Emerg Med. 1994;23(4):737-741.PubMedGoogle ScholarCrossref
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Attia  MW.  The blizzard of 1996: a pediatric emergency department.  Prehosp Emerg Care. 1998;2(4):285-288.PubMedGoogle ScholarCrossref
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Satterthwaite  PS, Atkinson  CJ.  Using “reverse triage” to create hospital surge capacity: Royal Darwin Hospital’s response to the Ashmore Reef disaster.  Emerg Med J. 2012;29(2):160-162.PubMedGoogle ScholarCrossref
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Moskop  JC, Sklar  DP, Geiderman  JM, Schears  RM, Bookman  KJ.  Emergency department crowding, part 2—barriers to reform and strategies to overcome them.  Ann Emerg Med. 2009;53(5):612-617.PubMedGoogle ScholarCrossref
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Van Cleve  WC, Hagan  P, Lozano  P, Mangione-Smith  R.  Investigating a pediatric hospital’s response to an inpatient census surge during the 2009 H1N1 influenza pandemic.  Jt Comm J Qual Patient Saf. 2011;37(8):376-382.PubMedGoogle ScholarCrossref
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Christian  MD, Joynt  GM, Hick  JL, Colvin  J, Danis  M, Sprung  CL; European Society of Intensive Care Medicine’s Task Force for intensive care unit triage during an influenza epidemic or mass disaster.  Chapter 7. Critical care triage. Recommendations and standard operating procedures for intensive care unit and hospital preparations for an influenza epidemic or mass disaster.  Intensive Care Med. 2010;36(suppl 1):S55-S64.PubMedGoogle ScholarCrossref
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Sprung  CL, Danis  M, Iapichino  G,  et al.  Triage of intensive care patients: identifying agreement and controversy.  Intensive Care Med. 2013;39(11):1916-1924.PubMedGoogle ScholarCrossref
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Ranse  J, Zeitz  K. Disaster triage. In: Powers  R, Daily  E, eds.  International Disaster Nursing. New York, NY: Cambridge University Press; 2010.
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Timbie  JW, Ringel  JS, Fox  DS,  et al.  Evidence Report/Technology Assessment Number 207: Allocation of Scarce Resources During Mass Casualty Events. Rockville, MD: Agency for Healthcare Research and Quality; 2012. AHRQ publication 12-E006-EF. http://www.effectivehealthcare.ahrq.gov/ehc/products/400/1151/EvidenceReport207_Allocation-of-Scarce-Resources_FinalReport_20120716.pdf. Accessed May 6, 2016.
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Scottish Government. Pandemic influenza: surge capacity and prioritisation [sic] in health services. Part 12: admission to, utilization of and discharge from services. http://www.gov.scot/Publications/2008/10/28141252/12. Published October 2008. Accessed May 6, 2016.
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Boyer  EW, Fitch  J, Shannon  M.  Pediatric Hospital Surge Capacity in Public Health Emergencies. Rockville, MD: Agency for Healthcare Research and Quality; January 2009. AHRQ publication 09-0014. Accessed January 10, 2017.
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Antommaria  AHM, Sweney  J, Poss  WB.  Critical appraisal of: triaging pediatric critical care resources during a pandemic: ethical and medical considerations.  Pediatr Crit Care Med. 2010;11(3):396-400.PubMedGoogle ScholarCrossref
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US Department of Homeland Security. Homeland Security Presidential Directive/HSPD-21: public health and medical preparedness. https://www.fas.org/irp/offdocs/nspd/hspd-21.htm. Published October 2007. Accessed May 6, 2016.
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Devereaux  AV, Dichter  JR, Christian  MD,  et al; Task Force for Mass Critical Care.  Definitive care for the critically ill during a disaster: a framework for allocation of scarce resources in mass critical care: from a Task Force for Mass Critical Care summit meeting, January 26-27, 2007, Chicago, IL.  Chest. 2008;133(5)(suppl):51S-66S.PubMedGoogle ScholarCrossref
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Hick  JL, Barbera  JA, Kelen  GD.  Refining surge capacity: conventional, contingency, and crisis capacity.  Disaster Med Public Health Prep. 2009;3(2)(suppl):S59-S67.PubMedGoogle ScholarCrossref
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Kanter  RK.  Strategies to improve pediatric disaster surge response: potential mortality reduction and tradeoffs.  Crit Care Med. 2007;35(12):2837-2842.PubMedGoogle ScholarCrossref
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Davis  DP, Poste  JC, Hicks  T, Polk  D, Rymer  TE, Jacoby  I.  Hospital bed surge capacity in the event of a mass-casualty incident.  Prehosp Disaster Med. 2005;20(3):169-176.PubMedGoogle ScholarCrossref
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Nigrovic  LE, Kuppermann  N, Macias  CG,  et al; Pediatric Emergency Medicine Collaborative Research Committee of the American Academy of Pediatrics.  Clinical prediction rule for identifying children with cerebrospinal fluid pleocytosis at very low risk of bacterial meningitis.  JAMA. 2007;297(1):52-60.PubMedGoogle ScholarCrossref
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Pollack  MM, Ruttimann  UE, Getson  PR.  Pediatric risk of mortality (PRISM) score.  Crit Care Med. 1988;16(11):1110-1116.PubMedGoogle ScholarCrossref
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Fuzak  JK, Elkon  BD, Hampers  LC,  et al.  Mass transfer of pediatric tertiary care hospital inpatients to a new location in under 12 hours: lessons learned and implications for disaster preparedness.  J Pediatr. 2010;157(1):138-143.e2.PubMedGoogle ScholarCrossref
Original Investigation
April 3, 2017

Effect of Reverse Triage on Creation of Surge Capacity in a Pediatric Hospital

Author Affiliations
  • 1Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 2Johns Hopkins Office of Critical Event Preparedness and Response, Johns Hopkins Institutions, Baltimore, Maryland
  • 3Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
 

Copyright 2017 American Medical Association. All Rights Reserved.

JAMA Pediatr. 2017;171(4):e164829. doi:10.1001/jamapediatrics.2016.4829
Key Points

Question  In the event of a disaster or serious hospital crowding, how much surge capacity within a hospital can be realized from various strategies, including safe early discharge (reverse triage) of pediatric inpatients?

Findings  In a year-long retrospective cohort study, 11% of 501 patients in aggregate from 7 hospital services were eligible for immediate low-risk reverse triage, with the psychiatry unit accounting for more than half of this incremental capacity. With multiple strategies that included reverse triage, an estimated 84% surge capacity could be realized within 4 days.

Meaning  Considerable surge capacity may exist in pediatric hospitals, and a low-risk reverse triage strategy realizes only a modest contribution.

Abstract

Importance  The capacity of pediatric hospitals to provide treatment to large numbers of patients during a large-scale disaster remains a concern. Hospitals are expected to function independently for as long as 96 hours. Reverse triage (early discharge), a strategy that creates surge bed capacity while conserving resources, has been modeled for adults but not pediatric patients.

Objective  To estimate the potential of reverse triage for surge capacity in an academic pediatric hospital.

Design, Setting, and Participants  In this retrospective cohort study, a blocked, randomized sampling scheme was used including inpatients from 7 units during 196 mock disaster days distributed across the 1-year period from December 21, 2012, through December 20, 2013. Patients not requiring any critical interventions for 4 successive days were considered to be suitable for low-risk immediate reverse triage. Data were analyzed from November 1, 2014, through November 21, 2016.

Main Outcomes and Measures  Proportionate contribution of reverse triage to the creation of surge capacity measured as a percentage of beds newly available in each unit and in aggregate.

Results  Of 3996 inpatients, 501 were sampled (268 boys [53.5%] and 233 girls [46.5%]; mean [SD] age, 7.8 [6.6] years), with 10.8% eligible for immediate low-risk reverse triage and 13.2% for discharge by 96 hours. The psychiatry unit had the most patients eligible for immediate reverse triage (72.7%; 95% CI, 59.6%-85.9%), accounting for more than half of the reverse triage effect. The oncology (1.3%; 95% CI, 0.0%-3.9%) and pediatric intensive care (0%) units had the least effect. Gross surge capacity using all strategies (routine patient discharges, full use of staffed and unstaffed licensed beds, and cancellation of elective and transfer admissions) was estimated at 57.7% (95% CI, 38.2%-80.2%) within 24 hours and 84.1% (95% CI, 63.9%-100%) by day 4. Net surge capacity, estimated by adjusting for routine emergency department admissions, was about 50% (range, 49.1%-52.6%) throughout the 96-hour period. By accepting higher-risk patients only (considering only major critical interventions as limiting), reverse triage would increase surge capacity by nearly 50%.

Conclusions and Relevance  Our estimates indicate considerable potential pediatric surge capacity by using combined strategic initiatives. Reverse triage adds a meaningful but modest contribution and may depend on psychiatric space. Large volumes of pediatric patients discharged early to the community during disasters could challenge pediatricians owing to the close follow-up likely to be required.

Introduction

Concern has been raised regarding the adequacy of hospital capacity to care for large numbers of ill or injured patients at the time of a disaster or other event with a significant health effect. Of particular concern is the ability to care for abrupt, large volumes of children given the particular vulnerabilities and specific medical care requirements of the pediatric population.1 In addition, many general hospitals are not adequately prepared to treat critically ill children.2,3 However, empirical research on effective means to use current resources and ensure adequate surge capacity for pediatric patients is lacking.

Surge capacity is defined as the maximum potential augmentation of resources beyond routine to provide treatment to the sudden unexpected influx of a large number of patients.4 Several general strategies to increase surge capacity of hospital beds have been advanced, including canceling elective admissions, opening unstaffed or unlicensed beds, exploiting unconventional space, and altering standards of care. A new strategy, labeled reverse triage, has been explored in the adult population.4 Reverse triage is a utilitarian ethical concept (ie, greatest good for the greatest number) wherein inpatients at low risk for untoward events would be discharged or transferred back to the community, giving inpatients and individuals affected by the disaster equal consideration for inpatient resources.4,5 However, early disposition, even among low-risk patients, would have implications for general and pediatric specialists, with the potential need for incremental monitoring of such patients in the immediate aftermath of early discharge. On the other hand, this strategy can be used not only in disasters but also during severe hospital crowding to allow improved and timely access for patients.

Experimental data and reports on adult patients appear to indicate that reverse triage can significantly augment surge bed capacity while conserving resources.4,6,7 Depending on the hospital type, 33% to nearly 50% of capacity could be realized by using a reverse triage strategy set to the lowest risk levels.

However, concepts developed for adults may not be readily applicable to pediatric populations.2,3,8,9 A system based on risk stratification for the application of reverse triage in the pediatric population was recently developed.10 Accordingly, we sought to estimate the potential contribution of reverse triage to surge capacity for low-risk pediatric patients and to begin to identify patient characteristics associated with reverse triage eligibility.

Methods
Setting

The Children’s Center of the Johns Hopkins Hospital is a tertiary inner-city teaching hospital in Baltimore, Maryland, that serves local residents and out-of-state referrals. It also includes the State of Maryland Pediatric Trauma and Pediatric Burn Centers. A total of 160 beds are distributed among the following 7 pediatric units: pediatric care/clinical research, infant and toddler care, adolescent care, school age and burn care, pediatric oncology, child and adolescent psychiatry, and the pediatric intensive care unit (ICU). The pediatric ICU has a capacity of 40 beds, and the remaining units each have 20 beds (160 beds total). The neonatal ICU was not included. During the year of study (December 21, 2012, through December 20, 2013), of 8965 patients admitted, 6282 (70.1%) of all inpatients were from our catchment area, whereas only 653 (7.3%) were from boundary states and 116 (1.3%) from 100 or more miles away; 4426 (49.4%) were insured by Medicaid, and 1824 (20.3%) had commercial insurance. The study was judged to be exempt by the Johns Hopkins institutional review board, which waived the need for informed consent.

Design

A blocked randomization scheme similar to previous work4 was used to control for pediatric unit type, season, and day of the week. We used MATLAB software (MathWorks, Inc) to generate 196 mock disaster dates during the 1-year study period. Thus, each unit was sampled 28 times during the year, with 12.5% of the eligible census chosen to meet sample size requirements (explained below). Although the study was performed in 2015, the last year of complete data was 2013 owing to an institutional change in the electronic medical record system in 2014. The randomization scheme incorporated the following criteria: each pediatric service was sampled an equal number of times per season, mock disaster days could not occur within 7 days of each other for a given unit, and no 2 pediatric units could have the same mock disaster date.

Data Sources

Patient-related data were obtained from detailed review of the electronic medical records and billing data. Individual floor census, staffed beds, and licensed unstaffed beds were gleaned from hospital administrative data.

Selection of Study Participants and Data

All patients present in the selected unit according to census records on the designated mock disaster day, denoted as T0, were eligible for the study.4 Data were collected for as many as 7 days if a sampled individual remained an inpatient. We defined T1 to T7 as the end of each sequential day. Data obtained for each patient included basic demographic information, admission date, previous admission within 30 days of the admission date, inpatient unit type, source of admission (elective, nonelective, or admitting service), discharge diagnosis, disposition, discharge date, and pediatric critical interventions (PCIs) as described below. We extracted the electronic medical record data in deidentified form into a study database. Owing to the forced entry nature of the electronic medical record, no data fields were missing.

Safe Early Discharge

The concept of safe early discharge has been previously described.6,10 A previously convened panel determined a list of PCIs that, if not initiated or withdrawn, could result in a consequential medical event. A consequential medical event was defined as unexpected death, irreversible impairment, or reduction of function occurring within 96 hours after withdrawing or failing to initiate a PCI aimed at stabilizing, ameliorating, or alerting and monitoring for the presence of the medical condition for which the PCI would normally be applied.6,10 The concepts of critical interventions and PCIs have been previously described.4,6,10 Pediatric-specific critical interventions, as previously published, were developed by an expert panel and are shown in Table 1.10 The only modification for the purposes of this study was the addition of intermittent aerosolized medication to account for variation in aerosolized medication use.

Statistical Analysis

Data were analyzed from November 1, 2014, through November 21, 2016. A minimum evaluation duration of 4 days (approximately 96 hours) was based on Joint Commission requirements and experiences with high-impact disasters11,12 showing that facilities should expect to sustain medical services for as long as 96 hours without expectation of significant external aid. Pediatric patients were judged to be eligible for early discharge if they did not require a PCI from T0 through T4 (ie, from the start of the mock disaster through the end of day 4). The PCIs were previously risk classified as major, moderate, or minor.10 A sensitivity analysis was performed ignoring minor- and moderate-risk PCIs to determine the incremental benefit of taking on the incremental risk for a consequential medical event.

Based on pilot data, the sample size requirement to statistically discern a minimum 5% safe early discharge, using an α value of .05 and a β value of .20, was calculated to be 451 patients.13,14 Univariate and multivariate logistic regression models were used to identify variables associated with early discharge eligibility with STATA software (version 12.10; StataCorp).

Surge capacity was hierarchically ordered as follows (for T0 through T4): staffed open beds, routine planned discharges, reverse triage, and unstaffed licensed beds. Full census (100% occupied) considered only actual staffed beds. Unstaffed licensed beds were considered to be incremental. The model assumed that all elective admissions and transfers (unrelated to the disaster) were stopped at T1. Although the literature indicates some reduction in emergency department (ED) admissions during disasters,15-17 the full historical effect of ED admissions was factored.

Results

A total of 3996 pediatric patients were identified across all units for all mock disaster dates. Sixty medical records (1.5%) were unavailable for analysis and replaced according to the sample algorithm. The final sample consisted of 501 patients (268 boys [53.5%] and 233 girls [46.5%]; mean [SD] age, 7.8 [6.6] years).

Demographic data are available in Table 1. Of the total sample, 239 patients (47.7%) were younger than 5 years, 64 (12.8%) were aged 5 to 9 years, 91 (18.2%) were aged 10 to 14 years, and 107 (21.4%) were 15 years or older. The demographic distributions of patients in the psychiatry unit were similar to those shown in Table 1 with the exception of age. The 44 patients in the psychiatry unit were considerably older; 41 (93.2%) were 10 years or older, and 21 (47.7%) were 15 years or older. The sample age distribution was similar to that publicly available from the National Hospital Discharge Survey for 2010.18 Two hundred seventeen patients (43.3%) were white, and 189 (37.7%) were black. The ED was the most likely route of admission (199 [39.7%]), followed by elective admissions (177 [35.3%]), outside hospital transfers (96 [19.2%]), and postpartum admissions (29 [5.8%]). One hundred twenty-two participants (24.4%) had a previous admission within 30 days of their admission date. The mean (SD) and median lengths of stay (excluding the pediatric ICU) were 22.7 (39.2) and 8 (interquartile range, 4-23) days, respectively, and the mean (SD) and median lengths of stay after disaster onset (T0) were 8.3 (15.9) and 3 (interquartile range, 1-8) days, respectively.

The estimated proportions of pediatric patients eligible for early discharge (in addition to patients designated for actual discharge) by T1 through T4 were 10.8% (95% CI, 8.1%-13.5%) by T1, 12.6% (95% CI, 9.7%-15.5%) by T2, 12.9% (95% CI, 9.9%-15.7%) by T3, and 13.2% (95% CI, 10.2%-16.1%) by T4. The psychiatry unit had the most patients eligible for immediate discharge (72.7%; 95% CI, 59.6%-85.9%). The oncology unit (1.3%; 95% CI, 0.0%-3.9%) and the pediatric ICU had the least, with none eligible in the latter (Figure 1). Sensitivity analysis excluding the psychiatry unit revealed that incremental capacity was 4.8% (95% CI, 2.9%-6.8%) by T1 and 7.0% (95% CI, 4.7%-9.3%) by T4. After excluding the pediatric ICU, reverse triage would have netted 13.8% (95% CI, 10.4%-17.3%) incremental capacity by T1 and 16.7% (95% CI, 13.0%-20.4%) by T4.

A total of 404 patients (80.6%) had at least 1 PCI during the 96-hour period of interest (T0-T4). Of those with no PCIs at T1, 96.9% (relative risk, 0.04) were discharged or unlikely to require further intervention during the next 4 days. In the hierarchic ordering of PCIs, 42.3% had at least 1 major PCI; 36.1%, at least 1 moderate PCI; and 2.2%, at least 1 minor PCI (Table 2). Of the 4608 total recorded interventions, the most common (42.1%) was medication administration (continuous intravenous, intermittent intravenous, intermittent aerosolized, parenteral pain, or combined), followed by supplemental oxygen (14.3%) and intravenous fluids (10.8%). Accepting a higher risk by eliminating minor PCIs from consideration had only a small effect on reverse triage (12.4% by T1 and 15.6% by T4). Considering only major critical interventions as exclusions to reverse triage considerably increased the projected effect of reverse triage (41.9% by T1 and 49.9% by T4).

The effect of all basic surge-creating strategies is shown in Figure 2. An aggregate mean 17.8% (95% CI, 15.3%-20.4%) of staffed beds were immediately available at T0. Routine discharges added 17.0% (95% CI, 13.7%-20.2%) of capacity within 24 hours (T1) and accounted for 41.1% (95% CI, 36.8%-45.4%) of all capacity by T4. Staffing available licensed beds on all services would have added a further 12.1% (95% CI, 1.1%-23.2%). Thus, total surge capacity of 57.7% (95% CI, 38.2%-80.2%) could be realized within 24 hours (T1), potentially increasing to 84.1% (95% CI, 63.9%-100%) by T4. Routine daily ED admissions would have curtailed surge capacity by 8.5% (95% CI, 7.9%-9.2%). Assuming none of these patients would be discharged before T4, total net surge capacity accounting for ED admissions for all subsequent days (T1-T4) would be about 50% (range, 49.1%-52.6%) (Figure 2). The odds ratios for independent variables revealed that groups aged 5 to 12 years (2.21; 95% CI, 1.23-3.98) and 13 years or older (2.29; 95% CI, 1.37-3.85) and male patients (0.57; 95% CI, 0.37-0.89) were associated with a higher likelihood of early discharge eligibility (Table 3).

Discussion

The ability to create surge capacity within pediatric hospitals remains a major concern. With caveats attendant to a single-site study, this analysis suggests that significant surge capacity can be realized in relatively short order by using a combination of strategies. Gross surge capacity that can be realized from simultaneously using all strategies is apparently sizeable, with 57.7% realized and/or reclaimed capacity within 24 hours and 84.1% by day 4 (Figure 2). Even with factoring uncurtailed routine ED admissions, a considerable net incremental capacity of about 50% is potentiated. Unlike the adult population, among whom routine ED admissions are expected to decrease during disasters, the pediatric experience is less clear15-17; thus, we made no adjustment to historic admission patterns.

Unlike a previous study with adults,4 the effect of reverse triage in a children’s hospital appears to be more modest. Previous data revealed near-immediate creation of surge capacity of 33% (excluding ICUs) for adult inpatients at our academic center from a similar reverse triage experiment.4 Reverse triage in this report appears to augment surge capacity by a modest 10.8% in the immediate aftermath of a declared mock disaster and no higher than 13.1% by the end of 4 days.

However, more than half of the potential effect of reverse triage appears to be dependent on patients in the psychiatry unit. The expert pediatric panel creating the conditions for early discharge reasoned that such patients could be discharged to the home and community with a modicum observation period of 96 hours.10 The system proposed herein identifies suitable patients for reverse triage and is meant to assist clinical judgment, not completely replace it. In addition, considerable stopgaps exist because relatively minor PCIs, such as psychiatric monitoring or even assistance with daily living, preclude low-risk reverse triage (Table 2). Another concern is that units such as psychiatry are not readily interchangeable to receive general medical patients. However, every hospital’s disaster plan needs to identify surge space, and readily functioning units are preferable to nonmedical space, such as cafeterias. Although psychiatry staff may not have the full skill set to care for acutely ill medical patients, care of children with concurrent medical problems on such units are facilitated, and the unit could prove to be suitable for stable patients who continue to require certain PCIs. In our sample, 5 patients requiring moderate or major PCIs received treatment in the psychiatry unit.

The concept of reverse triage as a capacity-creating strategy is gaining adherents,19-24 including official government agencies in the United States25 and elsewhere.26 Reverse triage has also been advocated for the pediatric population.27 However, as a vulnerable population, pediatric patients deserve special ethical consideration.9,28 The concept of reverse triage during disasters advances a utilitarian ethical approach to resource allocation, which may be very uncomfortable to apply to children already hospitalized.5 Homeland Security Presidential Directive/HSPD-21 calls for this approach and does not exclude children.29 Others30-32 have advanced similar concepts for the distribution of particular scarce resources during crises. A reverse triage strategy for all but the most obviously low-risk patients for whom discharge was being contemplated anyway may require alterations in standards of care, such as the shifting of certain interventions and protocols from the hospital to the home.5,33

The basis for the methods used in our study reveals a more risk-averse approach for a reverse triage strategy among pediatric than among adult patients.6,10 Expert panelists have argued that risks associated with early discharge in pediatrics should be no higher than for regularly discharged patients,10 whereas a higher risk of early discharge is tolerated among adults.6 In the model presented herein, eliminating lower-risk (ie, minor) interventions from consideration would have little effect. However, a surge capacity of 40% to 50% attributable to reverse triage could be realized with a more liberal approach, considering only higher-risk (major) interventions as exclusions. In the face of a disaster, utilitarian ethics supersede, and the health risk among admitted patients should be considered in light of the risk among those affected by the disaster.5 Some investigators34 advocate liberalizing pediatric discharge criteria to create pediatric surge capacity, and others10,35,36 believe that this strategy is necessary. Such practice, even during crises, may yet require regulatory or legislation protection for clinicians and health care facilities.

Although data support the anticipated surge capacity creation from reverse triage in the adult population,19,37,38 real experience in the pediatric population is limited.21 One uncontrolled study21 that attempted to apply reverse triage principles during an influenza-related spike in pediatric volume noted limited success because no criteria were in place for guidance and no method of application or central organization existed to guide the process. This finding is not too surprising because reverse triage in pediatric facilities that do not have a preplanned, accepted reverse triage protocol is not likely to have a high level of success during a crisis.

The ultimate goal of this line of investigation is to develop a system of risk prediction for early discharge similar to those of other pediatric risk prediction tools (eg, the Pneumonia Patient Outcomes Research Team,39 Apgar score, Bacterial Meningitis Score in children,40 and the predictive risk stratification model41). If a dispassionate system could be developed for assigning risk, rational allocation of scarce or limited resources at times of disaster or severe hospital crowding could be implemented.

Limitations

Our suggested approach to reverse triage remains relatively novel but well within espoused disaster principles. However, our investigative methods have not been validated by the clinical impressions of health care providers or clinicians or by other independent assessment. Some patients without apparent need of PCIs may yet be unsuitable for early discharge for reasons not determinable by our study methods. The converse may also be true, and we did not judge whether PCIs were warranted. Thus, we do not imply that any risk stratification tool be a substitute for clinical evaluation. Transfer-of-care protocols should be outlined in the institution’s disaster surge plan.

The study involved a single pediatric center, which may have unique features. Still, most of the patients are from our local catchment area, reinforced by our overrepresentation of black children compared with the national average. On the other hand, age distribution mimics the national data, speaking for generalizability. If anything, the unique nature of our facility would skew data toward lower eligibility for safe, early discharge given the complexity represented by the referral population of children.

Finally, the reverse triage and other capacity-generating strategies may not be readily applicable in prolonged events, such as an influenza pandemic. Also, our purpose was not to factor the complexities inherent in discharge or patient transfer. However, these complexities have been shown to be managed rapidly (<12 hours) and safely.42

Conclusions

With consideration of multiple strategies, pediatric hospital surge capacity may be considerably more robust than currently appreciated. The reverse triage strategy among this pediatric patient population is more limited than previously shown for adults and may be sizably dependent on the psychiatry unit. However, reverse triage appears to provide a meaningful contribution to creating capacity without requiring incremental resources. Ultimately, such an approach could also offer a safe means to address severe hospital crowding.

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Article Information

Corresponding Author: Gabor D. Kelen, MD, FRCPC, Department of Emergency Medicine, Johns Hopkins University School of Medicine, 1830 E Monument St, Ste 600, Baltimore, MD 21287 (gkelen@jhmi.edu).

Accepted for Publication: November 30, 2016.

Published Online: February 6, 2017. doi:10.1001/jamapediatrics.2016.4829

Author Contributions: Drs Kelen and Troncoso had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Kelen, Troncoso, Trebach, Cole, Delaney, Jenkins, Fackler, Sauer.

Acquisition, analysis, or interpretation of data: Kelen, Troncoso, Trebach, Levin, Cole, Delaney, Jenkins, Sauer.

Drafting of the manuscript: Kelen, Troncoso, Trebach, Jenkins, Sauer.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Kelen, Troncoso, Trebach, Levin, Delaney, Jenkins, Sauer.

Obtained funding: Kelen.

Administrative, technical, or material support: Troncoso, Cole, Delaney.

Study supervision: Kelen, Troncoso, Jenkins, Fackler, Sauer.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported in part by cooperative agreement 2010-ST-061-PA0001 to the National Center for Study of Preparedness and Critical Event Response at Johns Hopkins University from the US Department of Homeland Security and by grant 1441209 from the National Science Foundation.

Role of the Funder/Sponsor: The funding agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: Any opinions, finding, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily represent the policy or position of the US Department of Homeland Security or the National Science Foundation.

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